[
    {
        "id": "osp-16345",
        "type": "article-journal",
        "title": "AdaptLSTM: Efficient Adaptive Online Learning for Cloud Workload Forecasting under Distribution Drift",
        "author": [
            {
                "family": "Miao",
                "given": "Xinhua"
            },
            {
                "family": "Yang",
                "given": "Bowei"
            },
            {
                "family": "Cai",
                "given": "Zhengong"
            }
        ],
        "URL": "https://omanscience.com/en/articles/adaptlstm-efficient-adaptive-online-learning-for-cloud-workload-forecasting-under-distribution-drift",
        "language": "en",
        "issued": {
            "date-parts": [
                [
                    2026
                ]
            ]
        },
        "abstract": "Accurate workload forecasting is critical for elastic resource provisioning in web-scale cloud services, where distribution shifts driven by viral content, product launches, and user behavior degrade offline-trained models rapidly. Naive online learning recovers accuracy but incurs prohibitive per-step compute cost. We propose AdaptLSTM, an adaptive online framework that detects drift via validation-calibrated thresholds and applies selective, targeted updates. On the Alibaba Machine Trace, AdaptLSTM recovers 54\\% of Naive Online's improvement at 20\\% cost ($2.7\\times$ efficiency, $p=0.002$ over 10 seeds). On the more volatile Container Trace, it achieves 96\\% at 20\\% cost ($4.8\\times$ efficiency, $+75\\%$ MAE reduction over Static). Unlike classical drift detectors (ADWIN, DDM, Page-Hinkley) which fail to trigger on regression-scale error streams, AdaptLSTM fires 42 times over 301 steps and outperforms matched-budget baselines. Wall-clock profiling shows $1.33\\times$ throughput gain and 45\\% update-time reduction. The framework is model-agnostic: identical Pareto patterns hold for LSTM, GRU, and Transformer backbones."
    }
]